---
res:
  bibo_abstract:
  - "Classical machine learning techniques often struggle with overfitting and unreliable
    predictions when exposed to novel conditions. Introducing causality into the modelling
    process offers a promising way to mitigate these challenges by enhancing predictive
    robustness. However, constructing an initial causal graph manually using domain
    knowledge is time-consuming, particularly in complex time series with numerous
    variables. To address this, causal discovery algorithms can provide a preliminary
    causal structure that domain experts can refine. This study investigates causal
    feature selection with domain knowledge using a data center system as an example.
    We use simulated time-series data to compare \r\ndifferent causal feature selection
    with traditional machine-learning feature selection methods. Our results show
    that predictions based on causal features are more robust compared to those derived
    from traditional methods. These findings underscore the potential of combining
    causal discovery algorithms with human expertise to improve machine learning applications.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: David Ricardo
      foaf_name: Zapata Gonzalez, David Ricardo
      foaf_surname: Zapata Gonzalez
      foaf_workInfoHomepage: http://www.librecat.org/personId=105506
  - foaf_Person:
      foaf_givenName: Marcel
      foaf_name: Meyer, Marcel
      foaf_surname: Meyer
      foaf_workInfoHomepage: http://www.librecat.org/personId=105120
  - foaf_Person:
      foaf_givenName: Oliver
      foaf_name: Müller, Oliver
      foaf_surname: Müller
      foaf_workInfoHomepage: http://www.librecat.org/personId=72849
  dct_date: 2025^xs_gYear
  dct_language: eng
  dct_subject:
  - Causal Machine Learning
  - Causality in Time Series
  - Causal Discovery
  - Human-Machine  Collaboration
  dct_title: Bridging the gap between data-driven and theory-driven modelling – leveraging
    causal machine learning for integrative modelling of dynamical systems@
...